In these datasets, the objects are represented by vectors. Thus, to obtain the pairwise similarity matrix \(\mathbf {X}\) , we first compute the pairwise squared Euclidean distances between the vectors and obtain matrix \(\mathbf {D}\) . Then, as proposed in [1]}, we convert the pairwise distances \(\mathbf {D}\)  to the similarity matrix \(\mathbf {X}\)  via \(\mathbf {X}_{ij} = \max (\mathbf {D}) - \mathbf {D}_{ij} + \min (\mathbf {D})\) , where the \(\max (.)\)  and \(\min (.)\)  operations respectively give the maximum and the minimum of the elements in \(\mathbf {D}\) . An alternative transformation is an exponential function in the form of \(\mathbf {S}_{ij} = \exp (-\frac{\mathbf {X}_{ij}}{\sigma ^2})\) , which requires fixing the free parameter \(\sigma \)  in advance. However, this task is nontrivial in unsupervised learning and the appropriate values of \(\sigma \)  coincide in a very narrow range [2]}. The other alternative is the cosine similarity, which suits better to textual and document datasets. On our datasets, we consistently obtain better results with the aforementioned transformation.
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